OBJECTIVE:
To assess the frequency of screening for potential adverse outcomes in hospitalizations
of the Brazilian Unified Health System.METHODS: A retrospective study, including all hospital admissions of
adults in medical clinics (n = 3,565,811) and surgical clinics (n = 2,614,048)
in Brazil in 2007. The Hospital Information System was used as a source of information.
The measurement of adverse events was based on screening for eleven clinical
conditions, as defined by previous international studies, recorded in the secondary
diagnosis field. We performed bivariate and multivariate analysis to investigate
associations between adverse events, death (dependent variable) and other variables
such as age, use of the intensive care unit and performance of surgery.RESULTS: The frequency obtained for both clinic types was 3.6 potential
adverse events per 1,000 admissions, with a greater frequency in medical clinics
(5.3 per 1,000) than in surgery clinics (1.3 per 1,000). There were differences
in the profile of hospital admissions between the two clinics: medical clinics
were characterized by a predominance of older adults, longer average length
of stay, higher mortality rate and lower total cost of hospitalization. The
most common potential adverse outcome was hospital-acquired pneumonia. Cardiac
arrest had a higher risk of death (OR = 5.76) compared to other potential adverse
outcomes. Increased cost for hospitalizations was associated with sepsis. The
conditions used as the screening criteria were associated with greater odds
of death even after the introduction of variables such as use of intensive care
and surgery.CONCLUSIONS: The high frequency of adverse outcomes in hospital admissions
indicates a need to develop monitoring strategies and to improve quality of
care for improved patient safety.

Patient safety
has increasingly attracted attention since the publication of the book "To Err
is Human" by the Institute of Medicine in 2000.9 Given the relevance
of this topic, campaigns, programs and projects were launched to guide actions,
promote best practices, reduce harm related to unsafe practices and encourage
the development of harmful event and error reporting mechanisms.10
According to the World Health Organization (WHO)ª
(2009), "patient safety is the reduction of risk of unnecessary
harm associated with health care to an acceptable minimum."
Acceptable minimum refers to current knowledge and findings available and the
context within which care is provided. In order to create safer health settings,
concern for patient safety should include errors in health care, especially
those related to avoidable adverse events.14

WHO (2009) defines
an adverse event as an incident which results in harm to
a patient.ª A systematic review of studies on adverse events showed a
9.2% mean incidence of adverse events, 43.5% mean preventable death rate and
7.4% death rate associated.5 In addition to physical consequences,
harm caused to a patient is associated with irreversible stressful ethical processes;
health costs due to adverse events are a serious loss with prolonged hospital
stay and increased mortality; and lagging lawsuits resulting in financial, organizational,
and moral losses.6 Unsafe patient care can amount to a loss of credibility
of health services and poor relationship between patients and providers; an
increase in financial and social costs, and potentially undermine achieving
the expected results.b

Despite efforts
to develop a classification,18 there is no consensus on the definition
of patient safety. Some authors define an adverse event as a synonym of an adverse
outcome.13,20 Rivard et al16 claim that adverse outcome
is a broader term that include adverse events and other health care outcomes
such as death, disability, and cost, among others.

There is a lack
of scientific production on adverse events in Brazil and it has become a focus
of attention only recently.6,12 Many studies assessing the occurrence
of adverse events are based on medical records as a source of information. However,
countries such as the United States,8,13 and Belgium20
that have built comprehensive databases use administrative data for screening
adverse events and assessing health care outcomes and patient safety indicators.1,19,20,22,23
Administrative data can particularly provide summarized information at reduced
cost and time, offering new opportunities for assessing and monitoring the occurrence
of adverse health events.23 The use of administrative databases to
assess patient safety is still incipient in Brazil. Rozenfeld17 (2007)
was the first to study adverse drug events using information available from
the Brazilian National Hospital Database (SIH-SUS). SIH-SUS was originally developed
as a hospital services payment system and now is used as a source of information
on health care and hospital morbidities.2,21

The present study
aimed to assess the frequency of screeners of adverse outcomes in hospital admissions
in the Brazilian National Health System (SUS).

METHODS

Quantitative retrospective
study based on SIH-SUS data. The study included 6,179,859 admissions of medical
and surgical inpatients aged 17 years or more who were admitted in the SUS in
Brazil in 2007.

Data of abridged
files by federal unit were obtained from SIH-SUS. We chose to analyze hospital
admission forms (AIH) type 1, known as "regular," regardless of length of stay.
This exclusion criterion was applied because this study was designed to analyze
only acute cases (short hospital stay). The assessment of the interrelationship
between length of stay, case severity, and complications related to care in
patients requiring long-term care becomes more complex when a screening method
is used based on administrative data, as in the present study. SIH-SUS type
5 forms, known as "continuance forms," are mainly used in the specialty of psychiatry
and long-term care. This form was not used in surgical admissions, and 2,295
type 5 forms were used in medical admissions (0.06% of all medical admissions)
regardless of age. All admissions of patients younger than 18 years were excluded.
Of 8,714,148l admissions of adults during the study year, 6,247,891 (71.7%)
were medical and surgical; and 68,032 admissions with the same coding for principal
and secondary diagnosis were excluded. A total of 6,179,859 admissions of medical
and surgical inpatients were analyzed.

We chose here to
use the term adverse outcome, i.e., unfavorable or undesirable outcome of patient
care. A screening method was applied to assess conditions suspected to be consequences
(adverse outcomes) of the care provided. Eleven adverse outcomes were identified
based on the work by Needleman et al13 (2002) and Van Den Heede et
al19 (2006). These conditions are screeners of potential adverse
outcomes and include: urinary tract infection; pressure ulcers; hospital-acquired
pneumonia; shock/cardiac arrest; upper gastrointestinal bleeding; hospital-acquired
sepsis; deep venous thrombosis; central nervous system complications; surgical
wound infection; pulmonary failure and metabolic derangement. The main assumption
of this approach is that these outcomes can be prevented by quality nursing
care.13

Studies13,20
have coded these conditions according to the International Classification of
Diseases - 9th Revision, Clinical Modification (ICD-9-CM). Since
this classification is not used in Brazil, screeners of adverse outcomes were
coded according to the International Statistical Classification of Diseases
and Related Health Problems, 10th Revision (ICD-10). The ICD-9-CM
codes available in Van Den Heede et al study19 (2006) were adapted
and converted into the ICD-10 codes (Table 1). For that,
it was sought equivalences between each diagnostic category and definition of
inclusion and exclusion criteria. This process was carried out by a specialist
trained in disease coding. Two screeners, hospital-acquired pneumonia and pulmonary
failure, were coded J81 (pulmonary edema, not otherwise specified) of the ICD-10
because this code was duplicate in an earlier adaptation using ICD-9-CM. As
there is insufficient diagnostic information in SIH-SUS, which can certainly
underestimate hospital morbidity rates, we chose not to exclude 697 cases with
diagnostic information coded J81 due to the expected low frequency of screeners.
In the present studied the 11 adverse outcomes in both medical and surgical
inpatients were used in a different way from previous studies13,20
that applied wound infection, pulmonary failure, and metabolic derangement for
surgical inpatients only. A computer program was used to find this information
in the secondary diagnosis field of SIH-SUS data using ICD-10 codes.

The strategy of
analysis involved identifying screeners of adverse outcomes in both medical
and surgical inpatients and a description of average length of hospital stay,
death rate, and average reimbursement amount. Bivariate analyses were carried
out to compare the risk of death for each screener and by specialty. The risk
of death by screener was compared between medical and surgical patients.

Logistic regression
was used to assess the association between screeners of adverse outcomes and
death, adjusted for patient risk and care-related characteristics. This modeling
was performed in three consecutive stages that included: (1) variables for risk
adjustment of case severity, (2) screeners of adverse outcomes, (3) care-related
characteristics. At the first stage, case severity was described based on demographic
variables (age and gender), principal diagnosis, and type of admission (elective
or emergency). Age was used as a categorical variable, and all the rest were
dichotomous ones. The reference categories were male gender, Charlson index
of zero and elective admission. The Charlson index3 was applied to
the variable principal diagnosis, given that the population studied was heterogeneous
and this variable could not be used as categorical one. The Charlson index is
applied to secondary diagnosis data and contains 19 conditions defined based
on their association with the risk of death. The absolute relative risk was
used to weigh the effect of each medical condition on the patient's prognosis.11,15
The algorithm developed by Quan et al15 defined the ICD-10 codes
for each clinical condition of the Charlson index and was used to calculate
this score. Comorbidity severity was not measured as the space for recording
is limited to one secondary diagnosis, which was used as a source of information
on the frequency of screeners of adverse outcomes.

The second stage
of modeling included 11 screeners of adverse outcomes as dichotomous independent
variables (yes/no). The third and last stage included care-related variables
as follows: surgery (yes/no); length of stay (continuous variable); and intensive
care unit (ICU) care (yes/no). The predictive ability of the models was tested
with the use of C-statistics.

The statistical
package SPSS version 17.0 was used in the data analyses.

The study was approved
by the Research Ethics Committee of Escola Nacional de Saúde Pública
(Protocol Nº 227/09; December 4, 2009).

RESULTS

There was a higher
proportion of females among medical than surgical inpatients. The mean age was
higher and the proportion of elderly was greater among medical inpatients. A
secondary diagnosis was reported in 16.2% of all admissions, and was considerably
higher in surgical inpatients (24.8%). Length of stay was longer among medical
inpatients. The frequency of screeners of adverse outcomes was 0.36% higher
in medical inpatients. Most admissions were in private hospitals contracted
by SUS; however, there was a slightly higher proportion of medical inpatients
in public hospitals. The proportion of deaths was higher in medical inpatients,
as well as in emergency admissions and older patients (Table
2).

The most common
reason (principal diagnosis) for hospital admission was diseases of the circulatory
system (18.0%). The most common secondary diagnosis was external causes of morbidity
and mortality (5.4%). Circulatory diseases (23.4%) were the most frequently
reported condition as principal diagnosis in medical inpatients while the most
common secondary diagnoses were external causes (1.4%) and diseases of the circulatory
system (1.4%). Skin diseases (18.3%) and external causes of morbidity and mortality
(10.8%) were the most common principal and secondary diagnoses in surgical inpatients,
respectively.

The frequency of
screeners of adverse outcomes showed a varied distribution. Hospital-acquired
pneumonia was the most frequently reported one in both medical and surgical
inpatients. Other frequently reported screeners were urinary tract infections
and shock/cardiac arrest (Table
2).

The screeners of
adverse outcomes were mostly seen in public (52.9%) and philanthropic hospitals
(19.1%). The most common screener was shock/cardiac arrest in private hospitals
and hospital-acquired pneumonia in public hospitals. Except for shock/cardiac
arrest, all other screeners were mostly reported in public hospitals, with percentages
ranging from 49.6% (upper gastrointestinal bleeding) and 64.8% (surgical wound
infection). Private hospitals reported no cases of pressure ulcers and surgical
wound infection, probably associated with shorter hospital stay and higher transfer
rates.

The secondary diagnosis
was mostly reported in Southeast Brazil (55.7%), followed by the Northeast (18.2%),
and South (13.6%). The Northern State of Roraima (26.3%) showed the highest
reporting rates of secondary diagnosis, followed by São Paulo (25.0%)
and Brasília (22.4%). The 11 screeners of adverse outcomes were mostly
reported in the Southeast, ranging from 73.8% (metabolic derangement) and 92.2%
(surgical wound infection). The states of São Paulo and Rio de Janeiro
showed the highest frequency in this region. No deep venous thrombosis cases
were reported in the Northern region.

Hospital-acquired
sepsis and deep venous thrombosis were the most costly conditions in both medical
and surgical inpatients, but in the latter the average reimbursement amount
for hospital-acquired pneumonia was also significant (Table
3). Pressure ulcer, sepsis and hospital-acquired pneumonia were associated
with the longest hospital stays. Pressure ulcer was associated with an excess
of eight days of hospital stay compared to the average stay for all screeners.
Shock/cardiac arrest showed the highest crude death rate and risk of death (odds
ratio [OR] = 5.76) compared to other screeners in both medical and surgical
inpatients, followed by hospital-acquired sepsis. Hospital costs were higher
in surgical inpatients.

Length of stay,
reimbursement amount and death rate were higher in admissions with reporting
of screeners of potential adverse outcomes (Table
3) when compared to hospitalizations without screeners recorded showing
a length of stay of 5.1 days (SD = 7.4), an average reimbursement amount of
R$ 724.04 (SD = 1,650.05) and a death rate of 5.5%. Cases without screeners
had lower length of stay and hospital death than those admissions with reporting
of screeners. The average reimbursement amounts by screeners were higher in
surgical inpatients.

The logistic regression
models for predicting death included the variables studied in three blocks (Table
4). The risk model (Table 4, Model 1) showed adequate
discriminatory power (C = 0.73). The variables associated with patient risk
were significant with OR indicating higher risk of death.

The inclusion of
screeners of potential adverse outcomes (Table 4, Model 2)
did not change the OR found in Model 1. The inclusion of descriptive care-related
variables did not significantly change the OR in Model 2; a greater effect was
seen for cases of hospital-acquired sepsis (Table 4, Model
3). ICU care showed an OR = 7.45, indicating greater disease severity. There
were no significant changes in the models after the inclusion of the variables
in each block; the ORs decreased slightly, except for screeners of shock/cardiac
arrest and sepsis. Only OR of pulmonary failure was not statistically significant
(Table 4, Models 2 and 3). The final model showed an adequate
discriminatory power (C-statistic = 0.80, 95%CI 0.79;0.80).

DISCUSSION

This is a study
with a tracking approach, i.e., designed to identify potential conditions associated
to quality of care and patient safety.7 A tracking approach comprises
an initial assessment that requires a second assessment to ensure the occurrence
of a given outcome and to identify major explanatory factors and intervention
actions to prevent recurrence.7 The risk adjustment is a key element
because the outcome of care is a product of patient characteristics, adequacy
of the care process and random errors.7

The 11 adverse
outcomes defined in previous international studies13,20 were used
for measuring potential adverse outcomes sensitive to proper nursing care.13,20
The frequency of screeners reported in the SIH-SUS in medical and surgical adult
inpatients was 3.6/1,000 hospital admissions in Brazil in 2007. A higher frequency
of screeners was found among medical (5.3/1,000) than surgical inpatients (1.3/1,000).
International studies13,20 have found greater overall frequency and
by screener. The profile of admissions varied by specialty, with a predominance
of older inpatients, longer hospital stays and higher death rate in medical
inpatients. These data corroborate the literature4,7,11 that describes
an association of chronic condition, comorbidity, and disease severity in the
elderly with increased risk of death and adverse outcomes.

The frequency of
each screener of adverse outcomes varied in both specialties studied. In the
bivariate analysis shock/cardiac arrest had a higher risk of death (OR 5.76,
95%CI 5.28;6.28) compared to all other screeners reported in both medical and
surgical inpatients. Inpatients with screeners of adverse outcomes showed higher
average hospital stay, higher average reimbursement amount, and greater death
rates. Studies13,20 have found a higher frequency of urinary tract
infection, which contrasts with our finding of higher frequency of hospital-acquired
pneumonia. However, other comparisons were not possible due to different methods
and strategies used.

Despite limitations
related to the source of information used, the risk of death adjusted to patient
risk factors was associated with the presence of screeners. This association
remained even after the inclusion of care-related variables, which highlights
the importance of monitoring these events over time and by principal diagnosis
or specific surgical procedure. The current study was limited by its purpose
and design and the quality of patient- and care-related variables. The assessment
of screeners of adverse outcomes, as with clinical performance indicators, indirectly
shows quality of care since patient care was not assessed. The screeners of
adverse outcomes are a primary tool that can be used to identify potential cases
or hospitals at risk of providing care services of inadequate quality or below
the expected standard. These screeners include medical conditions that do not
allow to discriminating the relative importance of case severity and care quality
issues and the interaction between these factors.

The present study
also has limitations inherent to the use of administrative databases as a source
of information.23 The validity of screeners of potential adverse
outcomes relies on the completeness and accuracy of diagnostic codes reported
in the databases. The use of information from secondary databases restricts
the type and the scope of variables studied, although this approach is widely
used in comparative analyses of hospital performance. It is a relevant limitation
considering there is insufficient hospital morbidity information available in
the Brazilian administrative database. There is only a single field for reporting
secondary diagnoses but as there is no information on their time of occurrence
it does not allow to knowing whether a secondary diagnosis is a complication
or comorbidity. Another aspect is regarding adequacy and quality of information
reported in the SIH-SUS, especially regarding the limitation to a single secondary
diagnosis. Data quality issues including low reporting of secondary diagnosis
(16.8% for medical and surgical inpatients) may have affected accuracy of the
measures estimated. Furthermore, it also involved choosing a category to be
reported in cases with more than one secondary diagnosis. One of the criteria
for choosing a category may be related to requirements of the specific government
legislation and/or for reimbursement of hospital care.

The frequency of
screeners is directly associated to the quality of information reported, which
probably contributes to underestimated results. Failure to adjust for risk factors
of patients may have affected the results of the multivariate analysis. As there
was no variable available describing patients' morbidity profile at admission
it is difficult to discriminate between preexisting conditions and care-related
complications, especially in the event of specific medical conditions such as
cardiac arrest. However, this study was not designed to assess the validity
of screeners of adverse outcomes as a measure of quality of care. It aimed to
provide a detailed assessment of the quality of the care process.

The study showed
only the frequency of potential adverse outcomes, and thus it was not possible
to ascertain whether there was any adverse event, i. e., avoidable harm due
care and not the patient's disease. According to Needleman et al13
(2002) and Van Den Heede et al19 (2006), screeners of adverse outcomes
consist of conditions that are potentially sensitive to nursing care, suggesting
an association between high levels of nursing care and reductions in the rates
of deaths and adverse events. This study did not aim to assess this association,
but it would be an important aspect to be evaluated in further studies with
different data sources.

Some major aspects
of the current study should be noted. A nationwide analysis was conducted including
an array of hospital service providers within the Brazilian National Health
System. This study adapted screeners of adverse outcomes to the ICD-10 and explored
their use adjusted for patients' risk factors and care-related characteristics.
Although the adaptation of screeners to the ICD-10 may require further refinement
and expert validation it allow to promptly use the methodology tested20
in information systems based on the ICD-10 diagnostic coding. Moreover, it is
an innovative approach as there are few studies on adverse events in Brazil,
especially based on administrative data. The current study explored the feasibility
of using the SIH-SUS to assess adverse outcomes in health care and to measure
their effects on patients.

It is well-known
the extent, complexity, and incentive to administrative data production in more
developed countries,7, which has allowed more comprehensive assessments
of health systems. Quality of care and patient safety should be a priority in
the political agenda of governments and academia, as well as professional training
and retraining on the importance of reliable and complete recording of data
in information systems in health, which would render them more reliable. Regular
reassessments of information systems are needed for they can be used as effective
mechanisms for measuring the performance and quality of services provided. These
measures have an impact on public health services restructuring with a special
emphasis on quality of care, in addition to reimbursement of services. In conclusion,
despite the limitations of the current study approach and design, our findings
point to the importance of this issue in Brazil and the need for further research
and development of monitoring strategies and improvements targeted to patient
safety and quality of care provided in public hospitals, as seen in other countries.